Introduction to Artificial Intelligence for medical imaging and radiotherapy professionals

AI in healthcare, medical imaging and radiotherapy has developed rapidly over the last decade. This course presents the basic elements of Artificial Intelligence (AI) in the context of medical imaging and radiotherapy. It will offer you some background knowledge on all key contemporary AI topics and how these can affect your professional practice and workflow.

This is one of the first AI courses designed specifically for the practitioners in the medical imaging and radiotherapy workforce, and is integral in understanding and managing future changes in clinical practice, policy and research.

Who is it for?

This course is for recent medical imaging & radiotherapy, clinical practitioners, radiology managers, researchers & educators who wish to further their understanding of the basic principles & applications of AI in Medical Imaging & radiotherapy. These include radiographers, radiologists, clinical scientists, clinical imagers & scanner operators, etc. working in medical imaging & radiotherapy.

Timetable

Term 1 - 2026/7 (provisional dates)

On-campus Teaching:
Monday 9 November 2026
Tuesday 10 November 2026
Wednesday 11 November 2026
Thursday 12 November 2026
Friday 13 November 2026
Tutorial: 23 Novemeber 2026

Oral presentation: 11-12 January 2027

Benefits

This is the first course of its kind  in the UK and Europe, and its key takeaway is the ability to understand and manage future changes in medical imaging and radiotherapy practice.

This short course module is designed to be flexible in allowing you to study and reach your goals at your own pace. Our health CPD courses are credit-bearing modules that contribute to a University degree or award.

Transfer course credits towards postgraduate taught degree

As a health care professional, once you've completed this course you could offset 30 credits as part of a postgraduate programme, continuing your study with further modules to make up a Postgraduate Certificate (PGCert) 60 credits, Postgraduate Diploma (PGDip) 120 credits or Master of Science (MSc) 180 credits qualification (all credits must be awarded within five years of study commencing).

This course will discuss what AI is and offer detailed examples, presented by radiography, radiology, medical physics, data science, psychology practitioners, of how AI is applied in different contexts of medical imaging and radiotherapy practice.

It will further provide you with a smooth introduction to key concepts and terminologies of the principles of computer science used in clinical AI. Different manufacturers, vendors and AI start ups will present to you their latest innovations in the field of AI to stimulate discussion and debate and to allow you to understand and discuss practical implications. The ethics and governance of AI will also be analysed in relation to accountability and patient acceptability.

Knowledge and understanding:

• Examine and synthesise contemporary cutting-edge research and controversial AI topics, in relation to the background and rationale of your selected imaging modality within which AI operates.
• Explore the strengths, weaknesses, opportunities and threats of AI for the medical imaging and radiotherapy professionals, the NHS and the local clinical setting.
• Develop knowledge on how to expertly identify within current relevant literature a relevant clinical question to investigate, focusing on the impact of AI on clinical practice, workflows and patient pathway.

Skills:

• Justify the importance and relevance of your selected area of AI study
• Critically evaluate literature and other research work related to this topic
• Discuss recommendations for future research and its potential benefit to advancing clinical practice
• Demonstrate time-management skills through organisation of a self-directed learning approach.

Values and attitudes:

• Demonstrate respect for the intellectual work of others in the field by accurate referencing of published work
• Have regard for ethical issues relating to AI in health and social care settings
• Respect privacy and confidentiality
• Respect the opinions of others and behave with integrity
• Consider the ethical implications of AI in your practice.

Teaching

You’ll be learning in lectures, tutorials and online discussion and forum activities, alongside self-directed study.

Your lectures will be taught on campus, in person, alongside other postgraduate  students with expertise in different modalities of Medical Imaging (MRI, CT, Ultrasound, Nuclear medicine, Mammography) and radiotherapy. You will have the chance to learn from experts, researchers and innovators in the field of AI and ask them questions about their product and its applications in practice. You will also have the opportunity to debate AI topics, such as ethics, with leading figures from policy, practice and the professional bodies.

Assessment

You will receive formative feedback on a 300-word abstract of written work on an AI topic of your interest.

If you are doing this course as part of CPD there is no assessment attached. If you would like to gain the teaching credits for this course, you need to take the assessments as described below.

If you are doing this course as part of the master’s programme in MRI/CT you will be assessed by a written abstract on a case study and an oral presentation.

Learning Routes

This module has two routes namely, "CPD for attendance only" and "CPD for credits":

1. CPD “for attendance only” route: for students that wish to take this module as attendance only, this will grant you access to all lectures and online learning materials on Moodle. However, this route does not include the assessments, therefore you will not achieve any academic credits upon completion. A 25% discount is applied to the module fee. You will also receive a certificate of attendance at the end of the academic term.

2. CPD “for credits” route (Attendance & Assessment): for students that wish to take this module with assessments, this route will grant you access to all the lectures and online learning materials on Moodle. Upon successful completion of the assessment, you will have achieved the respective academic credits, as per individual module (there will range from 15 to 30 credits). No discount is applied to the module and the full fee will need to be paid.

For attendance only: 25% discount applied to the full fee (Please note: fees for overseas students will be a different rate, as per original fees on website)

For credit: (Attendance & Assessment): Full fees apply, as per individual module.

This course is provided by the School of Health & Medical Sciences.

Credits

This course is worth 30 credits toward eligible programmes.

This course is open as CPD to everyone in the UK , Europe and beyond or as part of our Master’s programme, with certain entry conditions, as you can find below and on the website.

• Applicants will normally hold a BSc (Hons) degree (2:2 or above) in Radiography or an equivalent medical imaging and radiotherapy discipline from a UK institution
• Appropriate professional qualifications e.g. Diploma of the College of Radiographers
• International qualifications in medical imaging and radiotherapy will have to be reviewed by our international team - they may only be acceptable if equivalent to a level 6 UK degree (BSc degree). Both originals and certified translations will have to be submitted to our programme team, before your application can be considered
• Non-standard entrants will be considered on a case-by-case basis, subject to availability of places on the programme (early application is recommended). Interviews might be arranged for these candidates, if the programme team feels necessary.

English requirements

If your first language is not English, one of the following is required:

• A first degree from a UK university
• A first degree from an overseas institution recognised by City, University of London as providing adequate evidence of proficiency in the English language, for example, from institutions from Australia, Canada or the United States of America.
• International English Language Test Service (IELTS) a score of 7.0 is required with no subtest below 7.0
• Pearson Test of English (Academic) score 72 required
• TOEFL 100 overall with 24 in Writing, 20 in Listening, 19 Reading and 20 Speaking
• Other evidence of proficiency in the English language, which satisfies the board of studies concerned, including registration with your professional regulator.

1. Artificial Intelligence in Cancer Imaging: Clinical Challenges and Applications
   Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath, Maryellen L. Giger, Nicolai J. Birkbak, Alireza Mehrtash, Tavis Allison, Omar Arnaout, Christopher Abbosh, Ian F. Dunn, Raymond H. Mak, Rulla M. Tamimi, Clare M. Tempany, Charles Swanton, Udo Hoffmann, Lawrence H. Schwartz, Robert J. Gillies, Raymond Y. Huang, Hugo J. W. L. Aerts.
   
   CA Cancer J Clin. 2019 Mar-Apr; 69(2): 127–157.
2. Artificial Intelligence in Radiology
   Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz, Hugo J. W. L. Aerts Nat Rev Cancer. 2018 Aug; 18(8): 500–510
3. Artificial Intelligence in Medical Imaging: Threat or Opportunity? Radiologists again at the forefront of innovation in medicine Filippo Pesapane, Marina Codari, Francesco Sardanelli
   Eur Radiol
4. Current Applications and Future Impact of Machine Learning in Radiology
   Garry Choy, Omid Khalilzadeh, Mark Michalski, Synho Do, Anthony E. Samir, Oleg S. Pianykh, J. Raymond Geis, Pari V. Pandharipande, James A. Brink, Keith J. Dreyer
   
   Radiology. August 2018; 288(2): 318–328.
5. Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement. J. Raymond Geis, Adrian Brady, Carol C. Wu, Jack Spencer, Erik Ranschaert, Jacob L. Jaremko, Steve G. Langer, Andrea Borondy Kitts, Judy Birch, William F. Shields, Robert van den Hoven van Genderen, Elmar Kotter, Judy Wawira Gichoya, Tessa S. Cook, Matthew B. Morgan, An Tang, Nabile M. Safdar, Marc Kohli
   Insights Imaging. 2019
6. Erik R. Ranschaert, Sergey Morozov, Paul R. Algra, Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks, 2019, Springer Verlag, Germany
7. Sofia Torre, Edwin Abdurakman, Jayne Morgan, Chris O’Sullivan, Sam Penry, Alison Rawlings, Richard Thorne, Jacquie Torrington, Soph Willis, Ric Khine, Christina Malamateniou. The Impact of AI in Radiography: A Snapshot of UK Radiographer Knowledge, Perceptions and Expectations. Radiography 2020 (in press)